What is the reduction in LLM API call overhead achieved by GNN-RAG compared to relation-path generation method

Abstract

Abstract: Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of answering natural questions grounding the reasoning to the information provided by the KG. Large Language Models (LLMs) are the state-of-the-art models for QA tasks due to their remarkable ability to understand natural language. On the other hand, Graph Neural Networks (GNNs) have been widely used for KGQA as they can handle the complex graph information stored in the KG. In this work, we introduce GN

Research Question

What is the reduction in LLM API call overhead achieved by GNN-RAG compared to relation-path generation methods on the WebQSP dataset?

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